Dr Shelda Sajeev

Adjunct Academic Status

College of Science and Engineering

place Flinders Medical Centre
GPO Box 2100, Adelaide 5001, South Australia

Shelda is a Postdoctoral Researcher at Flinders Digital Health Research Centre. Shelda is the project lead of one of the Shandong Flinders Joint project (Machine Learning for improving Heart Disease Risk Prediction) and principal investigator of AI – PREDICT (The utility of health data and predictive analytics in developing prognostic cardiac event and mortality models for patients presenting to the emergency department) project. Her research expertise in pattern recognition, image processing, medical image analysis, data analysis and machine learning.

Qualifications

Ph.D (Machine Learning and Medical Image Processing) at College of Science and Engineering, Flinders University (2019).

Masters in Computer Science and Engineering from VTU, India in 2012 .

Bachelor in Computer Science and Engineering from MG University, India in 2005.

Honours, awards and grants

Early Career Researcher Impact Seed Grant (2019)

Student Research Fellowship funded by National Cancer Institute, United States (2016).

Elaine Martin Travel Grant (2016).

Winner of 3 Minute Thesis Competition (CSEM, Flinders University, 2016).

Australian Postgraduate Award (2014-2017).

Teaching interests

2017 Tutor for STAT1122 Biostatistics

2016 Demonstrator for Engineering Programming.

2015 Tutor for Data Analysis Laboratory.

2015 Demonstrator for Computer Programming 1.

2014, 2015 and 2016 Tutor and Demonstrator for Fundamentals of Computing.

I have taught the following courses/topics in India:

  • Digital Image Processing for Masters students
  • Formal Models in Computer Science for Masters students
  • Csharp and Dot Net for Fourth year Bachelor students
  • Artificial Intelligence for Fourth year Bachelor students
  • Computer Concepts and C Programming for first year students.
Supervisory interests
Artificial intelligence and image processing
Data science
Medical image analysis
Pattern recognition